TBMI02 · 6 credits · Advanced level (A1N) · Elektroteknik, Medicinsk teknik
Medical Image Analysis
Active · next start HT 2026
80%
passed the latest ordinary exam
TEN2 · 13 Jan 2026 · 15 students
86%
passed the ordinary exams, all sittings over the last 5 years
TEN2 · 85 students
57%
Harder than 57% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 31 students.
Part of 7 programmes
- Master of Science in Biomedical Engineering
- Master of Science in Computer Science and Engineering
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Applied Physics and Electrical Engineering
- Master's Programme in Biomedical Engineering
Exam
TEN2 Written Examination
- 5
- 4
- 3
- U
Ordinary exams
86% passed · 85 students
- 80%15
- 88%16
- 86%14
- 76%21
- 100%19
Re-exams
No sittings with at least 5 students.
- Get 4 or 5: 53% (85 students)
Show small sittings (5)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 33%3
- 100%2
- 67%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory Work
- G
Main rounds
100% passed · 65 students
- 100%16
- 100%16
- 100%13
- 100%20
Later rounds
100% passed · 17 students
- 100%17
- Pass a later round: 100% (17 students), compared with 100% (65 students) on the ordinary exam
Show small sittings (6)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%1
- 100%2
- 100%2
- 100%2
About the course
Aim
The course aims to deepen the understanding of how medical images and volumes are analyzed. In particular, filter optimization, description of local structures, image denoising, image registration and image segmentation are covered. After the course the student will be able to:
- Describe and explain multidimensional filter optimization, and optimize filters with respect to requirements in the spatial domain as well as the frequency domain
- Describe and explain different local structure descriptors, and estimate them from images
- Describe and explain different methods for image denoising, and use a local structure description to perform image denoising using adaptive filtering
- Describe and explain traditional as well as deep learning-based methods for image segmentation, and implement a deep learning model for segmentation
- Describe and explain different transformation models and similarity measures for image registration, and implement a registration algorithm
- Describe and explain how fMRI data are analyzed, and implement a full pipeline for fMRI analysis
Content
Multidimensional Fourier analysis, local structure analysis in 2D, 3D and 4D (3D + time), image denoising, image registration, image segmentation using adaptive contours and surfaces.
- Examiner
- Anders Eklund
- Scheduled hours
- 48 h
- Main field of study
- Elektroteknik, Medicinsk teknik